Update app.py

app.py

@@ -1,22 +1,17 @@
 import gradio as gr
 import numpy as np
 import random
-import os
-import requests  # For calling Hugging Face's Inference API
 import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline, DPMSolverSDEScheduler
 import torch
 
-# Get Hugging Face API key from environment variable
-huggingface_api_key = os.getenv("HUGGINGFACE_API_KEY")
-
-if huggingface_api_key is None:
-    raise ValueError("Hugging Face API key is not set. Please set the 'HUGGINGFACE_API_KEY' environment variable.")
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"  # Replace with your model ID
+model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl" #Replace to the model you would like to use
 
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
 
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe.scheduler = DPMSolverSDEScheduler.from_config(pipe.scheduler.config, algorithm_type="dpmsolver++", solver_order=2, use_karras_sigmas=True)
@@ -25,52 +20,23 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-# Function to enhance the prompt using Hugging Face's Inference API
-def enhance_prompt(prompt):
-    hf_model_id = "EleutherAI/gpt-neo-1.3B"  # You can choose a different model
-    api_url = f"https://api-inference.huggingface.co/models/{hf_model_id}"
-    
-    headers = {
-        "Authorization": f"Bearer {huggingface_api_key}"
-    }
-    
-    payload = {
-        "inputs": f"Enhance this prompt: {prompt}",
-        "parameters": {"max_new_tokens": 50, "temperature": 0.7}
-    }
-    
-    response = requests.post(api_url, headers=headers, json=payload)
-    
-    if response.status_code != 200:
-        raise Exception(f"Failed to enhance prompt: {response.text}")
-    
-    result = response.json()
-    enhanced_prompt = result[0]['generated_text']
-    
-    return enhanced_prompt
-
-# Inference function with automatic prompt enhancement
-@spaces.GPU  # [uncomment to use ZeroGPU]
+@spaces.GPU #[uncomment to use ZeroGPU]
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-    
-    # Automatically enhance the prompt using Hugging Face's API
-    enhanced_prompt = enhance_prompt(prompt)
-    
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    
+        
     generator = torch.Generator().manual_seed(seed)
     
-    # Generate the image using the enhanced prompt
     image = pipe(
-        prompt=enhanced_prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator
-    ).images[0]
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
+    ).images[0] 
     
     return image, seed
 
@@ -80,7 +46,7 @@ examples = [
     "A delicious ceviche cheesecake slice",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
     max-width: 640px;
@@ -90,9 +56,12 @@ css = """
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# Text-to-Image Gradio Template")
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        """)
         
         with gr.Row():
+            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -106,6 +75,7 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
+            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
@@ -124,12 +94,13 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
+                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Default width for the model
+                    value=1024, #Replace with defaults that work for your model
                 )
                 
                 height = gr.Slider(
@@ -137,16 +108,17 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Default height for the model
+                    value=1024, #Replace with defaults that work for your model
                 )
             
             with gr.Row():
+                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,  # Default guidance scale for the model
+                    value=0.0, #Replace with defaults that work for your model
                 )
                 
                 num_inference_steps = gr.Slider(
@@ -154,20 +126,18 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Default inference steps for the model
+                    value=2, #Replace with defaults that work for your model
                 )
         
         gr.Examples(
-            examples=examples,
-            inputs=[prompt]
+            examples = examples,
+            inputs = [prompt]
         )
-    
-    # Handle button clicks and prompt submission
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, seed]
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
     )
 
-demo.queue().launch()
+demo.queue().launch()